Cognitive Science

While the field of perceptual learning has mostly been concerned with low- to middle-level changes to perceptual systems due to experience, we consider high-level perceptual changes that accompany learning in science and mathematics. In science, we explore the transfer of a scientific principle (competitive specialization) across superficially dissimilar pedagogical simulations. We argue that transfer occurs when students develop perceptual interpretations of an initial simulation and simply continue to use the same interpretational bias when interacting with a second simulation. In arithmetic and algebraic reasoning, we find that proficiency in mathematics involves executing spatially explicit transformations to notational elements. People learn to attend mathematical operations in the order in which they should be executed, and the extent to which students employ their perceptual attention in this manner is positively correlated with their mathematical experience. For both science and mathematics, relatively sophisticated performance is achieved not by ignoring perceptual features in favor of deep conceptual features, but rather by adapting perceptual processing so as to conform with and support formally sanctioned responses. These “Rigged Up Perceptual Systems” (RUPS) offer a promising approach to educational reform.

Categorical perception (CP) is the phenomenon by which the categories possessed by an observer influences their perception. Experimentally, CP is revealed when an observer’s ability to make perceptual discriminations between things is better when those things belong to different categories rather than the same category, controlling for the physical difference between the things. We consider several core questions related to CP: Is it caused by innate and/or learned categories, how early in the information processing stream do categories influence perception, and what is the relation between ongoing linguistic processing and CP? CP for both speech and visual entities are surveyed, as are computational and mathematical models of CP. CP is an important phenomenon in cognitive science because it represents an essential adaptation of perception to support categorizations that an organism needs to make. Sensory signals that could be linearly related to physical qualities are warped in a non-linear manner, transforming analog inputs into quasi-digital, quasi-symbolic encodings.

perceptual learning is important for two reasons—because it is perceptual and because it is learning. Changes to perception are particularly important because they affect all subsequent cognitive processes that occur downstream. There is good evidence, both neurophysiological and behavioral, that perceptual learning can involve early changes to the primary visual, auditory, and somatosensory cortices. One might feel that the early perceptual system ought to be hardwired—it is better not to mess with it if it is going to be depended upon by all processes later in the information processing stream. There is something right with this intuition, but it implicitly buys into a ‘‘stable foundations make strong foundations’’ assumption that it is appropriate for houses of cards, but probably not for flexible cognitive systems. For better models of cognition, we might turn to Birkenstock shoes and suspension bridges, which provide good foundations for their respective feet and cars by flexibly deforming to their charges. Just as a suspension bridge provides better support for cars by conforming to the weight loads, perception supports problem solving and reasoning by conforming to these tasks.

If perceptual learning is crucially perceptual, it is also crucially learning. Consistent with the ripples of downstream influence that early perceptual changes exert, perceptual systems should generally be designed to change slowly and conservatively, so as not to disrupt their downstream consumers. For this reason, this book’s focus on perceptual expertise is appropriate. Expertise typically requires at least 10 years to attain (Ericsson, Krampe, & Tesch-Römer, 1993), sufficient time to influence perception, not simply decision trees or explicitly memorized strategies. The protracted time course of acquiring new perceptual tools is certainly frustrating for those in the business of judging wines, rock samples, cell structures, dives, or manufacturing flaws. One of the reasons why wisdom can’t be simply told (Bransford, Franks, Vye, & Sherwood, 1989) but rather must be lived is that wisdom is frequently perceptual and thus must be built into one’s neurological wiring.

It might not be immediately clear why the topic of comparison warrants a whole chapter in a book on human thinking. Of course, we are often required to make decisions that involve comparing two or more alternatives and assessing their relative value. Which car should I buy? Which job is more suited to my long-term goals? Would I rather have the soup or the salad? But in the grand scheme of human cognition, it might seem that such processes could be relegated to a subheading in a chapter on decision making. In fact, comparison is one of the most integral components of human thought. Along with the related construct of similarity, comparison plays a crucial role in almost everything that we do. Furthermore, comparison itself is a powerful cognitive tool—in addition to its supporting role in other mental processes, research has demonstrated that the simple act of comparing two things can produce important changes in our knowledge.

Drawing on game-design principles and an underlying situated theoretical perspective, we developed and researched a 3D game-based curriculum designed to teach water quality concepts. We compared undergraduate student dyads assigned randomly to four different instructional design conditions where the content had increasingly level of contextualization: (a) expository textbook condition, (b) simplistic framing condition, (c) immersive world condition, and a (d) single-user immersive world condition. Results indicated that the 3D-dyad and 3D-single user conditions performed significantly better than the electronic textbook group on standardized items. The immersive-world dyad condition also performed significantly better than either the expository textbook or the descriptive framing condition on a performance-based transfer task, and performed significantly better than the expository textbook condition on standardized test items. Implications for science education, and consistent with the goals of this special issue, are that immersive game-based learning environments provide a powerful new form of curriculum for teaching and learning science.

We examine the interdependence between individual and group behavior surrounding a somewhat arbitrary, real world decision: selecting a name for
one's child. Using a historical database of the names given to children over the last century in the United States, we nd that naming choices are influenced by both the frequency of a name in the general population, and by its "momentum" in the recent past in the sense that names which are growing in popularity are preferentially chosen. This bias toward rising names is a recent phenomena: in the early part of the 20th century, increasing popularity of a name from one time period to the next was correlated with a decrease in future popularity. However, more recently this trend has reversed. We evaluate a number of formal models that detail how individual decision-making strategies, played out in a large population of interacting agents, can explain these empirical observations. We argue that cognitive capacities for change detection, the encoding of frequency in memory, and biases towards novel or incongruous stimuli may interact with the behavior of other decision makers to determine the distribution and dynamics of cultural tokens such as names.

In two studies, we find that participants are able to transfer strategies learned while interacting with a simulated physical system to a dissimilar and less perceptually-concrete domain. Interestingly, performance on the transfer task was completely unrelated to explicit knowledge of the structural correspondences between the systems. We suggest that direct interaction with a concrete system may lead to a kind of procedural knowledge that provides a good basis for analogical transfer.

Two experiments are reported. The first shows incomplete transfer of explicit categorical learning at a distance of 4.5 degrees of visual angle and the second is a control experiment with a non-learning task. The results suggest that some early visual plasticity takes place even in a simple, explicit categorical learning task. We claim that perceptual learning is a much more common phenomenon than believed before and that it plays an important role in everyday tasks including higher-level learning.

The resurgence of interest in collective behavior is in large part due to tools recently made available for conducting laboratory experiments on groups, statistical methods for analyzing large data sets reflecting social interactions, the rapid growth of a diverse variety of online self-organized collectives, and computational modeling methods for understanding both universal and scenario-specific social patterns. We consider case studies of collective behavior along four attributes: the primary motivation of individuals within the group, kinds of interactions among individuals, typical dynamics that result from these interactions, and characteristic outcomes at the group level. With this framework, we compare the collective patterns of noninteracting decision makers, bee swarms, groups forming paths in physical and abstract spaces, sports teams, cooperation and competition for resource usage, and the spread and extension of innovations in an online community. Some critical issues surrounding collective behavior are then reviewed, including the questions of ‘‘Does group behavior always reduce to individual behavior?’’ ‘‘Is ‘group cognition’ possible?’’ and ‘‘What is the value of formal modeling for understanding group behavior?’’

Categorization relies upon the vocabulary of features that comprise the target objects. Previous theoretical work (Schyns, Goldstone, & Thibaut, 1998) has argued this vocabulary may change through learning and experience. Goldstone (2000) demonstrated this perceptual learning during a categorization task when new features are added that create a single feature unit from multiple existing units. We present two experiments that expand on that work using whole-part judgments (Palmer, 1978) to elicit the feature representation learned through categorization. The implications for different classes of computational models of categorization are discussed.

This paper explores the hypothesis that schematic abstraction—rule following—is partially implemented through processes and knowledge used to understand motion. Two experiments explore the mechanisms used by reasoners solving simple linear equations with one variable. Participants solved problems displayed against a background that moved rightward or leftward. Solving was facilitated when the background motion moved in the direction of the numeric transposition required to solve for the unknown variable. Previous theorizing has usually assumed that such formal problems are solved through the repeated application of abstract transformation patterns (rules) to equations, replicating the steps produced in typical worked solutions. However, the current results suggest that in addition to such strategies, advanced reasoners often employ a mental motion strategy when manipulating algebraic forms: elements of the problem are “picked up” and “moved” across the equation line. This demonstration supports the suggestion that genuinely schematic reasoning could be implemented in perceptual-motor systems through the simulated transformation of referential (but physical) symbol systems.

Human groups exhibit poor performance in many social situations because task constraints promote either individual maximization behavior or diffusion of responsibility. We introduce a group task that tests human coordination when only a shared group goal exists. Without communication, group members submit numbers in an attempt to collectively sum to a randomly selected number. After receiving group feedback, members adjust their submitted numbers in the next round. Small groups generally outperform large groups, and for all groups, performance improves with task experience, and reactivity to feedback decreases over rounds. Our empirical results and computational modeling provide evidence for adaptive coordination in human groups despite minimal shared history and only indirect communication, and perhaps most interestingly, as the coordination costs increase with group size, large groups adapt through spontaneous role differentiation and self-consistency among members.

Previous group foraging research has shown that human groups sub-optimally distribute themselves to resources and display undermatching, with a smaller-than-expected proportion of individuals at the more abundant resource pool. In order to further explore these sub-optimalities, we extended a group foraging paradigm to test three variables: the effects of three resource pools, travel cost between pools, and the size of the pools. Although each condition led to undermatching, the conditions showed significant differences in the extent of undermatching, the frequency of switching between resource pools, the wealth inequality among foragers, and the comparative wealth inequality at different resource pools. The results for the three pool conditions suggest that human groups have difficulty in discriminating the relative value of resource pools. The results for the travel cost conditions indicate that human groups distribute themselves to resources more optimally when individuals can easily switch between pools, which is the opposite of the result found with foraging pigeons. Finally, the results for the pool size conditions indicate that larger pool sizes promote greater undermatching, apparently because individuals inefficiently compete over large areas rather than effectively parceling the pools into smaller, distinct regions.

Instruction abstracted from specific and concrete examples is frequently criticized for ignoring the context-dependent and perspectival nature of learning (e.g., Bruner, 1962, 1966; Greeno, 1997). Yet, in the effort to create personally interesting learning contexts, cognitive consequences have often been ignored. To examine what kinds of personalized contexts foster or hinder learning and transfer, three manipulations of perspective and context were employed to teach participants Signal Detection Theory (SDT). In all cases, application of SDT principles was negatively impacted by manipulations that encouraged participants to consider the perspective of the signal detector (the decision maker in SDT situations): by giving participants active detection experience (Experiment 1), biasing them to adopt a first-person rather than third-person perspective (Experiment 2), or framing the task in terms of a well-known celebrity (Experiment 3). These contexts run the risk of introducing goals and information that are specific to the detector’s point of view, resulting in sub-optimal understanding of SDT.

Understanding scientific phenomena in terms of complex systems principles is both scientifically and pedagogically important. Situations from different disciplines of science are often governed by the same principle, and so promoting knowledge transfer across disciplines makes valuable cross-fertilization and scientific unification possible. Although evidence for this kind of transfer has been historically controversial, experiments and observations of students suggest pedagogical methods to promote transfer of complex systems principles. One powerful strategy is for students to actively interpret the elements and interactions of perceptually grounded scenarios. Such interpretation can be facilitated through the presentation of cases alongside general principles, and by students exploring and constructing computational models of cases. The resulting knowledge can be both concretely grounded yet highly perspective-dependent and generalizeable. We discuss methods for coordinating computational and mental models of complex systems, the roles of idealization and concreteness in fostering understanding and generalization, and other complementary theoretical approaches to transfer.

One of the most important applications of grounded cognition theories is to science and mathematics education where the primary goal is to foster knowledge and skills that are widely transportable to new situations. This presents a challenge to those grounded cognition theories that tightly tie knowledge to the specifics of a single situation. In this chapter, we develop a theory learning that is grounded in perception and interaction, yet also supports transferable knowledge. A first series of studies explores the transfer of complex systems principles across two superficially dissimilar scenarios. The results indicate that students most effectively show transfer by applying previously learned perceptual and interpretational processes to new situations. A second series shows that even when students are solving formal algebra problems, they are greatly influenced by non-symbolic, perceptual grouping factors. We interpret both results as showing that high-level cognition that might seem to involve purely symbolic reasoning is actually driven by perceptual processes. The educational implication is that instruction in science and mathematics should involve not only teaching abstract rules and equations but also training students to perceive and interact with their world.

Human concept learning depends upon perception. Our concept of Car is built out of perceptual features such as “engine,” “tire,” and “bumper.” However, recent research indicates that the dependency works both ways. We see bumpers and engines in part because we have acquired Car concepts and detected examples of them. Perception both influences and is influenced by the concepts that we learn. We have been exploring the psychological mechanisms by which concepts and perception mutually influence one another, and building computational models to show that the circle of influences is benign rather than vicious.

Just as networks of neurons create structured thoughts beyond the ken of any individual neuron, so people spontaneously organize themselves into groups to create emergent organizations that no individual may intend, comprehend, or even perceive. Recent technological advances have provided us with unprecedented opportunities for conducting controlled, laboratory experiments on human collective behavior. We describe two experimental paradigms where we attempt to build predictive bridges between the beliefs, goals, and cognitive capacities of individuals and group-level patterns, showing how the members of a group dynamically allocate themselves to resources, and how innovations are spread in a social network. Agent-based computational models have provided useful explanatory and predictive accounts. Together, the models and experiments point to tradeoffs between exploration and exploitation, compromises between individuals using their own innovations and innovations obtained from their peers, and the emergence of group-level organizations such as population waves, bandwagon effects, and spontaneous specialization.

Our laboratory has been studying the emergence of collective search behavior from a complex systems perspective. We have developed an internet-based experimental platform that allows groups of people to interact with each other in real time on networked computers. The experiments implement virtual environments where participants can see the moment-to-moment actions of their peers and immediately respond to their environment. Agent-based computational models are used as accounts of the experimental results. We describe two paradigms for collective search – one in physical space and the other in an abstract problem space. The physical search situation concerns competitive foraging for resources by individuals inhabiting an environment consisting largely of other individuals foraging for the same resources. The abstract search concerns the dissemination of innovations in social networks. Across both scenarios, the group-level behavior that emerges reveals influences of exploration and exploitation, bandwagon effects, population waves, and compromises between individuals using their own information and information obtained from their peers.